Affine Subspace Representation for Feature Description

This paper proposes a novel Affine Subspace Representation (ASR) descriptor to deal with affine distortions induced by viewpoint changes. Unlike the traditional local descriptors such as SIFT, ASR inherently encodes local information of multi-view patches, making it robust to affine distortions while maintaining a high discriminative ability. To this end, PCA is used to represent affine-warped patches as PCA-patch vectors for its compactness and efficiency. Then according to the subspace assumption, which implies that the PCA-patch vectors of various affine-warped patches of the same keypoint can be represented by a low-dimensional linear subspace, the ASR descriptor is obtained by using a simple subspace-to-point mapping. Such a linear subspace representation could accurately capture the underlying information of a keypoint (local structure) under multiple views without sacrificing its distinctiveness. To accelerate the computation of ASR descriptor, a fast approximate algorithm is proposed by moving the most computational part (ie, warp patch under various affine transformations) to an offline training stage. Experimental results show that ASR is not only better than the state-of-the-art descriptors under various image transformations, but also performs well without a dedicated affine invariant detector when dealing with viewpoint changes.Comment: To Appear in the 2014 European Conference on Computer Visio

Ubic: Bridging the gap between digital cryptography and the physical world

Advances in computing technology increasingly blur the boundary between the digital domain and the physical world. Although the research community has developed a large number of cryptographic primitives and has demonstrated their usability in all-digital communication, many of them have not yet made their way into the real world due to usability aspects. We aim to make another step towards a tighter integration of digital cryptography into real world interactions. We describe Ubic, a framework that allows users to bridge the gap between digital cryptography and the physical world. Ubic relies on head-mounted displays, like Google Glass, resource-friendly computer vision techniques as well as mathematically sound cryptographic primitives to provide users with better security and privacy guarantees. The framework covers key cryptographic primitives, such as secure identification, document verification using a novel secure physical document format, as well as content hiding. To make a contribution of practical value, we focused on making Ubic as simple, easily deployable, and user friendly as possible.Comment: In ESORICS 2014, volume 8712 of Lecture Notes in Computer Science, pp. 56-75, Wroclaw, Poland, September 7-11, 2014. Springer, Berlin, German

Combining depth and intensity images to produce enhanced object detection for use in a robotic colony

Robotic colonies that can communicate with each other and interact with their ambient environments can be utilized for a wide range of research and industrial applications. However amongst the problems that these colonies face is that of the isolating objects within an environment. Robotic colonies that can isolate objects within the environment can not only map that environment in de-tail, but interact with that ambient space. Many object recognition techniques ex-ist, however these are often complex and computationally expensive, leading to overly complex implementations. In this paper a simple model is proposed to isolate objects, these can then be recognize and tagged. The model will be using 2D and 3D perspectives of the perceptual data to produce a probability map of the outline of an object, therefore addressing the defects that exist with 2D and 3D image techniques. Some of the defects that will be addressed are; low level illumination and objects at similar depths. These issues may not be completely solved, however, the model provided will provide results confident enough for use in a robotic colony

Determination of fatty acid composition in seed oil of rapeseed (Brassica napus L.) by mutated alleles of the FAD3 desaturase genes

One of the goals in oilseed rape programs is to develop genotypes producing oil with low linolenic acid content (C18:3, ≤3%). Low linolenic mutant lines of canola rapeseed were obtained via chemical mutagenesis at the Plant Breeding and Acclimatization Institute – NRI, in Poznan, Poland, and allele-specific SNP markers were designed for monitoring of two statistically important single nucleotide polymorphisms detected by SNaPshot analysis in two FAD3 desaturase genes, BnaA.FAD3 and BnaC.FAD3, respectively. Strong negative correlation between the presence of mutant alleles of the genes and linolenic acid content was revealed by analysis of variance. In this paper we present detailed characteristics of the markers by estimation of the additive and dominance effects of the FAD3 genes with respect to particular fatty acid content in seed oil, as well as by calculation of the phenotypic variation of seed oil fatty acid composition accounted by particular allele-specific marker. The obtained percentage of variation in fatty acid composition was considerable only for linolenic acid content and equaled 35.6% for BnaA.FAD3 and 39.3% for BnaC.FAD3, whereas the total percentage of variation in linolenic acid content was 53.2% when accounted for mutations in both genes simultaneously. Our results revealed high specificity of the markers for effective monitoring of the wild-type and mutated alleles of the Brassica napus FAD3 desaturase genes in the low linolenic mutant recombinants in breeding programs

Body weight, body mass index, overweight and obesity in consecutive cohorts of children at school entry in a community in Lower Bavaria 1997-2002

Objective: To study time trends of weight, body mass index, overweight and obesity of children at school entry and to analyze potential effects of changes in the structure of consecutive cohorts of children at school entry over time on these weight-related measures. Design: We studied height, weight and body mass index (BMI) in 6 consecutive cohorts (1997-2002) of children at school entry (N=6,420). Overweight and obesity were defined by internationally valid sex and age specific cut-off points. In addition to descriptive statistics for time trends we applied an analysis of covariance to estimate the impact of covariates on weight and BMI and logistic regression models for the impact of covariates on overweight and obesity. Results: Although we found an overall decrease of mean body weight (minus 9%), BMI (minus 9.5%), overweight (minus 7.4%) and obesity (minus 6.8%) between 1997 and 2002, there was a considerable variation in these measures between single years. The analysis of covariance showed significant impact of age, gender and year of examination on weight and BMI. Whereas there were significant differences in the proportion of overweight children between different age groups, the effect of age was not significant for the proportion of obesity. Multiple logistic regression models showed that age (OR, 2.8; 95% CI, 2.3-3.5) and female gender (OR, 1.3; 95% CI, 1.2-1.5) were significantly associated with overweight and significantly with obesity (age: OR, 1.8; 95% CI, 1.2-2.9; female gender: OR, 1.4; 95% CI, 1.0-1.99), respectively. In these models the years of examination of 1998 (OR, 1.9; 95 %CI, 1.5-2.5) and 1999 (OR, 2.5; 95% CI, 1.97-3.3) were significantly associated with overweight, and the year 1999 (OR, 2.9; 95% CI, 1.6-5.2) with obesity. Conclusions: Our study showed that changes in age and gender distribution have to be taken into account when time trends of weight, BMI, overweight and obesity are derived from investigations of children at school entry.Bernard Theodor Baune, Rafael Thomas Mikolajczyk, Heribert Stich, Alexander Kräme

Generic 3D Representation via Pose Estimation and Matching

Though a large body of computer vision research has investigated developing generic semantic representations, efforts towards developing a similar representation for 3D has been limited. In this paper, we learn a generic 3D representation through solving a set of foundational proxy 3D tasks: object-centric camera pose estimation and wide baseline feature matching. Our method is based upon the premise that by providing supervision over a set of carefully selected foundational tasks, generalization to novel tasks and abstraction capabilities can be achieved. We empirically show that the internal representation of a multi-task ConvNet trained to solve the above core problems generalizes to novel 3D tasks (e.g., scene layout estimation, object pose estimation, surface normal estimation) without the need for fine-tuning and shows traits of abstraction abilities (e.g., cross-modality pose estimation). In the context of the core supervised tasks, we demonstrate our representation achieves state-of-the-art wide baseline feature matching results without requiring apriori rectification (unlike SIFT and the majority of learned features). We also show 6DOF camera pose estimation given a pair local image patches. The accuracy of both supervised tasks come comparable to humans. Finally, we contribute a large-scale dataset composed of object-centric street view scenes along with point correspondences and camera pose information, and conclude with a discussion on the learned representation and open research questions.Comment: Published in ECCV16. See the project website http://3drepresentation.stanford.edu/ and dataset website https://github.com/amir32002/3D_Street_Vie

Visual 3-D SLAM from UAVs

The aim of the paper is to present, test and discuss the implementation of Visual SLAM techniques to images taken from Unmanned Aerial Vehicles (UAVs) outdoors, in partially structured environments. Every issue of the whole process is discussed in order to obtain more accurate localization and mapping from UAVs flights. Firstly, the issues related to the visual features of objects in the scene, their distance to the UAV, and the related image acquisition system and their calibration are evaluated for improving the whole process. Other important, considered issues are related to the image processing techniques, such as interest point detection, the matching procedure and the scaling factor. The whole system has been tested using the COLIBRI mini UAV in partially structured environments. The results that have been obtained for localization, tested against the GPS information of the flights, show that Visual SLAM delivers reliable localization and mapping that makes it suitable for some outdoors applications when flying UAVs

Visual Image Search: Feature Signatures or/and Global Descriptors

The success of content-based retrieval systems stands or falls with the quality of the utilized similarity model. In the case of having no additional keywords or annotations provided with the multimedia data, the hard task is to guarantee the highest possible retrieval precision using only content-based retrieval techniques. In this paper we push the visual image search a step further by testing effective combination of two orthogonal approaches – the MPEG-7 global visual descriptors and the feature signatures equipped by the Signature Quadratic Form Distance. We investigate various ways of descriptor combinations and evaluate the overall effectiveness of the search on three different image collections. Moreover, we introduce a new image collection, TWIC, designed as a larger realistic image collection providing ground truth. In all the experiments, the combination of descriptors proved its superior performance on all tested collections. Furthermore, we propose a re-ranking variant guaranteeing efficient yet effective image retrieval

Student perceptions of a healthy university

As complex environments within which individuals and populations operate, universities present important contexts for understanding and addressing health issues. The healthy university is an example of the settings approach, which adopts a whole system perspective, aiming to make places within which people, learn, live, work and play supportive to health and wellbeing. The UK Healthy Universities Network has formulated an online toolkit, which includes a self-review tool, intended to enable universities to assess what actions they need to take to develop as a healthy university. This paper presents findings from consultative research undertaken with students from universities in England, Scotland and Wales, which explored what they believe represents a healthy university. Methods Student surveys and focus groups were used to collect data across eleven universities in England, Scotland and Wales. A priori themes were used to develop our own model for a healthy university, and for the thematic coding phase of analysis. Findings A healthy university would promote student health and wellbeing in every aspect of its business from its facilities and environment through to its curriculum. Access to reasonably priced healthy food and exercise facilities were key features of a healthy university for students in this study. The Self Review Tool has provided a crucial start for universities undertaking the journey towards becoming a healthy university. In looking to the future both universities and the UK Healthy Universities Network will now need to look at what students want from their whole university experience, and consider how the Self Review Tool can help universities embrace a more explicit conceptual framework. Conclusion The concept of a healthy university that can tailor its facilities and supportive environments to the needs of its students will go some way to developing students who are active global citizens and who are more likely to value and prioritise health and wellbeing, in the short and long term through to their adult lives